Toward a Predictive Smart Parking System in IoT-enabled Cities

One of the main traffic problems that need to be taken care of is traffic congestion, which causes many harmful consequences such as air pollution and waste of fuel. The ineffectiveness of parking vehicles is the main reason for traffic congestion due to the shortage of parking spaces and the lack of guidance information leading to spending considerable time searching for parking spaces, which causes traffic delays. In this paper, we proposed a smart parking system that can predict parking availability based on long short-term memory (LSTM) network. The system then notifies the drivers about forecast information that help drivers save time in choosing parking lots. Subsequently, we deploy a license plate recognition (LPR) mechanism on the Jetson nano developer kit that automatically recognizes the vehicle's plate at the parking lot entrance. Experimental results show that LSTM can outperform the popular time series forecasting mechanisms (AutoTS, Darts) on the Birmingham parking lot dataset, and our LPR mechanism performance can reach 47fps in license detection on the Jetson nano developer kit.